What is the power of a genomic multidisciplinary team approach? A systematic review of implementation and sustainability.
Alan Sl MaRosie O'SheaLaura WeddClaire M Y WongRobyn V JamiesonNicole RankinPublished in: European journal of human genetics : EJHG (2024)
Due to the increasing complexity of genomic data interpretation, and need for close collaboration with clinical, laboratory, and research expertise, genomics often requires a multidisciplinary team (MDT) approach. This systematic review aims to establish the evidence for effectiveness of the genomic multidisciplinary team, and the implementation components of this model that can inform precision care. MEDLINE, Embase and PsycINFO databases were searched in 2022 and 2023. We included qualitative and quantitative studies of the genomic MDT, including observational and cohort studies, for diagnosis and management, and implementation outcomes of effectiveness, adoption, efficiency, safety, and acceptability. A narrative synthesis was mapped against the Genomic Medicine Integrative Research framework. 1530 studies were screened, and 17 papers met selection criteria. All studies pointed towards the effectiveness of the genomic MDT approach, with 10-78% diagnostic yield depending on clinical context, and an increased yield of 6-25% attributed to the MDT. The genomic MDT was found to be highly efficient in interpretation of variants of uncertain significance, timeliness for a rapid result, made a significant impact on management, and was acceptable for adoption by a wide variety of subspecialists. Only one study utilized an implementation science based approach. The genomic MDT approach appears to be highly effective and efficient, facilitating higher diagnostic rates and improved patient management. However, key gaps remain in health systems readiness for this collaborative model, and there is a lack of implementation science based research especially addressing the cost, sustainability, scale up, and equity of access.
Keyphrases
- quality improvement
- copy number
- systematic review
- healthcare
- primary care
- palliative care
- randomized controlled trial
- highly efficient
- electronic health record
- dna methylation
- gene expression
- big data
- high resolution
- case report
- weight loss
- insulin resistance
- machine learning
- health insurance
- pain management
- adipose tissue